NETME: on-the-fly knowledge network construction from biomedical literature

CovidTGI: A tool to investigate the temporal genetic instability of SARS-CoV-2 variants

The COVID-19 pandemic has underscored the need for fast and accurate epidemiology, particularly due to the high observed mutation frequency in SARS-CoV-2. This study aims to explore the evolution of SARS-CoV-2 through a global analysis. To facilitate a comparative analysis of temporal mutation data, we developed CovidTGI, a Shiny web application. CovidTGI provides insights into observed mutation frequencies and the temporal relationships among mutations across various clades in different geographical regions. Our tool relies on a database that includes 2 million samples obtained from the National Center for Biotechnology Information (NCBI), along with 500 in-house Sicilian samples collected between May 2021 and June 2022. From this smaller group of samples, we identified key variants that are prevalent within a specific clade. Our tool is designed to study the evolution of SARS-CoV-2, which clearly follows a complex trajectory. This complexity highlights the necessity for sophisticated tools like CovidTGI to understand and track the evolution of this virus.

BioTextQuest v2.0: An evolved tool for biomedical literature mining and concept discovery

The process of navigating through the landscape of biomedical literature and performing searches or combining them with bioinformatics analyses can be daunting, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related repositories. Herein, we present BioTextQuest v2.0, a tool for biomedical literature mining. BioTextQuest v2.0 is an open-source online web portal for document clustering based on sets of selected biomedical terms, offering efficient management of information derived from PubMed abstracts. Employing established machine learning algorithms, the tool facilitates document clustering while allowing users to customize the analysis by selecting terms of interest. BioTextQuest v2.0 streamlines the process of uncovering valuable insights from biomedical research articles, serving as an agent that connects the identification of key terms like genes/proteins, diseases, chemicals, Gene Ontology (GO) terms, functions, and others through named entity recognition, and their application in biological research. Instead of manually sifting through articles, researchers can enter their PubMed-like query and receive extracted information in two user-friendly formats, tables and word clouds, simplifying the comprehension of key findings. The latest update of BioTextQuest leverages the EXTRACT named entity recognition tagger, enhancing its ability to pinpoint various biological entities within text. BioTextQuest v2.0 acts as a research assistant, significantly reducing the time and effort required for researchers to identify and present relevant information from the biomedical literature.

NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph

MOTIVATION

The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging.

RESULTS

We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts-i.e. in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users-and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision-Recall metrics when compared to state-of-the-art approaches.

AVAILABILITY AND IMPLEMENTATION

https://netme.click/.

First public dataset to study 2023 Turkish general election

In the context of Turkiye's most recent parliamentary and presidential elections ("seçim" in Turkish), social media has played an important role in shaping public debate. It is of utmost importance to capture social media trends during the 2023 Turkish elections, since it uncovers a great deal of information of election propaganda, political debates, smear campaigns, and election manipulation by domestic and international actors. We provide a comprehensive dataset for social media researchers to study Turkish elections, develop tools to prevent online manipulation, and gather novel information to inform the public. We are committed to continually improving the data collection and updating it regularly leading up to the election. Using the #Secim2023 dataset, researchers can examine the social and communication networks between political actors, track current trends, and investigate emerging threats to election integrity. Our dataset and analysis code available through Harvard Dataverse and Github, respectively.

tRFUniverse: A comprehensive resource for the interactive analyses of tRNA-derived ncRNAs in human cancer

tRNA-derived ncRNAs are a heterogeneous class of non-coding RNAs recently proposed to be active regulators of gene expression and be involved in many diseases, including cancer. Consequently, several online resources on tRNA-derived ncRNAs have been released. Although interesting, such resources present only basic features and do not adequately exploit the wealth of knowledge available about tRNA-derived ncRNAs. Therefore, we introduce tRFUniverse, a novel online resource for the analysis of tRNA-derived ncRNAs in human cancer. tRFUniverse presents an extensive collection of classes of tRNA-derived ncRNAs analyzed across all the TCGA and TARGET tumor cohorts, NCI-60 cell lines, and biological fluids. Moreover, public AGO CLASH/CLIP-Seq data were analyzed to identify the molecular interactions between tRNA-derived ncRNAs and other transcripts. Importantly, tRFUniverse combines in a single resource a comprehensive set of features that we believe may be helpful to investigate the involvement of tRNA-derived ncRNAs in cancer biology.

The impact of vitamin D on cancer: A mini review

In this review, we summarize the most recent advances in vitamin D cancer research to provide molecular clarity, as well as its translational trajectory across the cancer landscape. Vitamin D is well known for its role in regulating mineral homeostasis; however, vitamin D deficiency has also been linked to the development and progression of a number of cancer types. Recent epigenomic, transcriptomic, and proteomic studies have revealed novel vitamin D-mediated biological mechanisms that regulate cancer cell self-renewal, differentiation, proliferation, transformation, and death. Tumor microenvironmental studies have also revealed dynamic relationships between the immune system and vitamin D's anti-neoplastic properties. These findings help to explain the large number of population-based studies that show clinicopathological correlations between circulating vitamin D levels and risk of cancer development and death. The majority of evidence suggests that low circulating vitamin D levels are associated with an increased risk of cancers, whereas supplementation alone or in combination with other chemo/immunotherapeutic drugs may improve clinical outcomes even further. These promising results still necessitate further research and development into novel approaches that target vitamin D signaling and metabolic systems to improve cancer outcomes.

Multi-layer network approach in modeling epidemics in an urban town

ABSTRACT

The last three years have been an extraordinary time with the COVID-19 pandemic killing millions, affecting and distressing billions of people worldwide. Authorities took various measures such as turning school and work to remote and prohibiting social relations via curfews. In order to mitigate the negative impact of the epidemics, researchers tried to estimate the future of the pandemic for different scenarios, using forecasting techniques and epidemics simulations on networks. Intending to better represent the real-life in an urban town in high resolution, we propose a novel multi-layer network model, where each layer corresponds to a different interaction that occurs daily, such as "household", "work" or "school". Our simulations indicate that locking down "friendship" layer has the highest impact on slowing down epidemics. Hence, our contributions are twofold, first we propose a parametric network generator model; second, we run SIR simulations on it and show the impact of layers.

BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis

The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.

Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining

Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format.